The field of the invention is nuclear magnetic resonance imaging methods and systems. More particularly, the invention relates to the reduction of image artifacts caused by patient motion during an MRI scan.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, Mz, may be rotated, or“dipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1 is terminated, this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx Gy and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
Acquiring magnetic resonance (MR) images may require a time period of seconds to minutes. Over this period, significant anatomical motion may occur—specifically, cardiac- and respiratory-induced motion. This motion produces artifacts that may significantly degrade image quality. A number of different techniques have been developed in order to compensate for the effects of this motion. These techniques attempt to either acquire data during periods of minimal motion, or to correct for the effects of motion when it does occur. For these techniques to compensate for the effects of motion, the motion itself must be known accurately throughout the data acquisition. In the past, bellows and navigator echoes placed on the diaphragm have been used to determine respiratory-induced motion. A shortcoming of this approach is that diaphragm position may not accurately reflect respiratory-induced motion at anatomy remote from the diaphragm. For cardiac-induced motion, ECG-waveforms have been used. The problem with ECG waveforms is that there may be substantial variation from one cardiac cycle to the next, particularly in patient populations. Consequently, the cardiac position may correspondingly vary from one cycle to the next. Overall, the drawback with previous motion compensation techniques is that they rely on indirect measures to infer the motion of the anatomy under investigation.
In an attempt to overcome these difficulties, there have been a number of attempts to utilize information from the acquired data simultaneously for motion compensation purposes. One technique extracts phase information from the central portion of spiral interleaves to detect in-plane spatial shifts of the anatomy. A similar approach has been developed using individual k-space lines. The problem with these approaches is that they require the assumption of rigid-body anatomical motion. This is a questionable assumption for respiratory-induced motion and an invalid one for cardiac-induced motion.
Recently, an adaptive averaging technique has been introduced that combines a real-time series of aliased, EPI images to produce a high signal-to-noise ratio (SNR), high-resolution image. In this technique, motion compensation is accomplished by utilizing data acquired only during periods of minimal motion. Such periods are identified by applying the cross-correlation template matching technique to each individual image frame. The advantage of this approach is that motion compensation is accomplished through a direct visualization of the anatomy. The disadvantage of this technique is that the resolution is limited by the amount of aliasing that is tolerable in the real-time EPI images. Also, at present, the identification of the optimal data acquisition periods is done in a semi-quantitative manner. Finally, this technique is restricted to two-dimensional (2D) imaging.